New York County Mental Health Utilization

Recipient Counts, Rates, and Expenditures 2006-2016

By State of New York [source]

About this dataset

This dataset offers insightful summary information regarding mental health services funded by Medicaid from Local Fiscal years 2006 to 2016. These reports provide insight into mental health service utilization, such as Comprehensive Outpatient Program Services and Community Support Program payments where applicable. With data refreshed on a monthly basis, these reports offer the opportunity to gain invaluable access to influential information about an important and often overlooked or undervalued aspect of the population’s collective wellbeing. Whether you are a public serviced provider looking for ways to better serve individuals or just someone wanting insight into population trends in mental health services, this dataset is sure to provide value. Carve out valuable time in your day as you explore its contents. Because it may just be that scholarly look at a how people access quality care that gives you pause to think more deeply about our society and your part within it!

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How to use the dataset

This dataset provides detailed summary information about mental health services utilization funded through Medicaid for various local fiscal years from 2006-2016. In order to use this dataset effectively, it is important to understand the different components of the data and what they represent.

The columns included in this dataset include Row Created Date Time, Service Year, OMH Region Code, OMH Region Label, County Label, Age Group,” “Rate Code Group,” “Recipient Count By County” “Count of Recipients By Rate Code Group And County,” and “Units Total. These columns offer valuable insight into various aspects of Medicaid-funded mental health service utilization by local fiscal year as well as specifics regarding recipient demographics such as county label and age group.

Once you have familiarized yourself with what each represent, you can use this data to conduct your analysis on how Medicaid-funded utilized has changed over time or how certain age groups or counties tend to utilize more/less services than others. You can also look at trends within the rate code group column and see which services are most commonly used by these populations.

In short, this dataset provides a wealth of useful information about organizations of mental health service utilization among New York's counties from 2006 - 2016 that can be further broken down into demographic units for further analysis if desired

Research Ideas

• Analyzing trends in service utilization for each county and how it changes over time to identify areas of greatest need and reinvestment.
• Correlating mental health service utilization with other economic, health, or education data points to provide insights into the overall well-being of a region.
• Leveraging geographical analysis tools such as GIS to map out mental health services across different districts and counties on an interactive platform that allows people to quickly find resources in their area

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: county-mental-health-profiles-2006-2016-1.csv | Column name | Description | |:-------------------------------------------------------|:--------------------------------------------------------------------| | Row Created Date Time | Date and time the row was created. (DateTime) | | Service Year | Year of service. (Integer) | | OMH Region Code | Code for the OMH region. (Integer) | | OMH Region Label | Label for the OMH region. (String) | | County Label | Label for the county. (String) | | Age Group | Age group of the recipient. (String) | | Rate Code Group | Group of rate codes. (String) | | **Recipient Count By Co...

Graph and download economic data for Resident Population in New York County, NY (NYNEWY1POP) from 1970 to 2024 about New York County, NY; New York; NY; residents; population; and USA.

The number of people living in urban neighborhoods has been rising in recent decades. This Commentary investigates changes in the number, ages, and financial status of those who have been moving into and out of urban neighborhoods, using data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel. I find that since 2000, the increase in urban populations is the result of young adults migrating into urban neighborhoods and senior citizens aging in place. Urban populations have also become more educated and well to do. While declining urban neighborhoods may still outnumber growing urban neighborhoods within some regions, urban leaders there can work toward population or tax base growth knowing that consumer tastes and national trends are favorable to those goals.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The main aim of this research is to study the criminal mobility of ethnic-based organized crime groups. The project examines whether organized crime groups are able to move abroad easily and to reproduce their territorial control in a foreign country, or whether these groups, and/or individual members, start a life of crime only after their arrival in the new territories, potentially as a result of social exclusion, economic strain, culture conflict and labeling. More specifically, the aim is to examine the criminal mobility of ethnic Albanian organized crime groups involved in a range of criminal markets and operating in and around New York City, area and to study the relevance of the importation/alien conspiracy model versus the deprivation model of organized crime in relation to Albanian organized crime. There are several analytical dimensions in this study: (1) reasons for going abroad; (2) the nature of the presence abroad; (3) level of support from ethnic constituencies in the new territories; (4) importance of cultural codes; (5) organizational structure; (6) selection of criminal activities; (7) economic incentives and political infiltration. This study utilizes a mixed-methods approach with a sequential exploratory design, in which qualitative data and documents are collected and analyzed first, followed by quantitative data. Demographic variables in this collection include age, gender, birth place, immigration status, nationality, ethnicity, education, religion, and employment status. Two main data sources were employed: (1) court documents, including indictments and court transcripts related to select organized crime cases (84 court documents on 29 groups, 254 offenders); (2) in-depth, face-to-face interviews with 9 ethnic Albanian offenders currently serving prison sentences in U.S. Federal Prisons for organized crime related activities, and with 79 adult ethnic Albanian immigrants in New York, including common people, undocumented migrants, offenders, and people with good knowledge of Albanian organized crime modus operandi. Sampling for these data were conducted in five phases, the first of which involved researchers examining court documents and identifying members of 29 major ethnic Albanian organized crime groups operating in the New York area between 1975 and 2013 who were or had served sentences in the U.S. Federal Prisons for organized crime related activities. In phase two researchers conducted eight in-depth interviews with law enforcement experts working in New York or New Jersey. Phase three involved interviews with members of the Albanian diaspora and filed observations from an ethnographic study. Researchers utilized snowball and respondent driven (RDS) recruitment methods to create the sample for the diaspora dataset. The self-reported criteria for recruitment to participate in the diaspora interviews were: (1) age 18 or over; (2) of ethnic Albanian origin (foreign-born or 1st/2nd generation); and (3) living in NYC area for at least 1 year. They also visited neighborhoods identified as high concentrations of ethnic Albanian individuals and conducted an ethnographic study to locate the target population. In phase four, data for the cultural advisors able to help with the project data was collected. In the fifth and final phase, researchers gathered data for the second wave of the diaspora data, and conducted interviews with offenders with ethnic Albanian immigrants with knowledge of the organized crime situation in New York City area. Researchers also approached about twenty organized crime figures currently serving a prison sentence, and were able to conduct 9 in-depth interviews.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Context

This dataset is a record of every building or building unit (apartment, etc.) sold in the New York City property market over a 12-month period.

Content

This dataset contains the location, address, type, sale price, and sale date of building units sold. A reference on the trickier fields:

• BOROUGH: A digit code for the borough the property is located in; in order these are Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5).
• BLOCK; LOT: The combination of borough, block, and lot forms a unique key for property in New York City. Commonly called a BBL.
• BUILDING CLASS AT PRESENT and BUILDING CLASS AT TIME OF SALE: The type of building at various points in time. See the glossary linked to below.

For further reference on individual fields see the Glossary of Terms. For the building classification codes see the Building Classifications Glossary.

Note that because this is a financial transaction dataset, there are some points that need to be kept in mind:

• Many sales occur with a nonsensically small dollar amount: $0 most commonly. These sales are actually transfers of deeds between parties: for example, parents transferring ownership to their home to a child after moving out for retirement.
• This dataset uses the financial definition of a building/building unit, for tax purposes. In case a single entity owns the building in question, a sale covers the value of the entire building. In case a building is owned piecemeal by its residents (a condominium), a sale refers to a single apartment (or group of apartments) owned by some individual.

Acknowledgements

This dataset is a concatenated and slightly cleaned-up version of the New York City Department of Finance's Rolling Sales dataset.

Inspiration

What can you discover about New York City real estate by looking at a year's worth of raw transaction records? Can you spot trends in the market, or build a model that predicts sale value in the future?

The report contains thirteen (13) performance metrics for City's workforce development programs. Each metric can be breakdown by three demographic types (gender, race/ethnicity, and age group) and the program target population (e.g., youth and young adults, NYCHA communities) as well. This report is a key output of an integrated data system that collects, integrates, and generates disaggregated data by Mayor's Office for Economic Opportunity (NYC Opportunity). Currently, the report is generated by the integrated database incorporating data from 18 workforce development programs managed by 5 City agencies. There has been no single "workforce development system" in the City of New York. Instead, many discrete public agencies directly manage or fund local partners to deliver a range of different services, sometimes tailored to specific populations. As a result, program data have historically been fragmented as well, making it challenging to develop insights based on a comprehensive picture. To overcome it, NYC Opportunity collects data from 5 City agencies and builds the integrated database, and it begins to build a complete picture of how participants move through the system onto a career pathway. Each row represents a count of unique individuals for a specific performance metric, program target population, a specific demographic group, and a specific period. For example, if the Metric Value is 2000 with Clients Served (Metric Name), NYCHA Communities (Program Target Population), Asian (Subgroup), and 2019 (Period), you can say that "In 2019, 2,000 Asian individuals participated programs targeting NYCHA communities. Please refer to the Workforce Data Portal for further data guidance (https://workforcedata.nyc.gov/en/data-guidance), and interactive visualizations for this report (https://workforcedata.nyc.gov/en/common-metrics).

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs su

Context

This dataset includes the nearest pickup and drop off city names for each trip record from New York City Taxi Trip Duration Competition.

Content

The dataset introduces two new columns namely "Nearest_PickupCity" and "Nearest_DropoffCity" in addition to the original trip features. The city names may not be the exact geo cities in some cases, they are the nearest city to the trip records, therefore the term "Nearest" describes them best.

Acknowledgements

Implemented the offline package Reverse Geocoder (author - Ajay Thampi ) to get these data attributes. The original package is developed by Richard Pennman.

Inspiration

The idea is that this extension to the NYC Trip data can provide interesting and informative city trends about the taxi trips in NYC area.

1. Which cities drive the taxi trip demand ?
2. Does the trip demand vary based on city as the day moves from morning to night?
3. Can pickup and drop off city information improve trip duration prediction ?

All feedback is welcome

These tables will stop being updated after June 1, 2023. COVID-19 vaccination reporting is expected to resume when a new COVID-19 vaccination formulation is authorized. As 4/22/2023, CDC recommends bivalent vaccine for everyone regardless of age and whether or not the person has had prior monovalent vaccine. This table shows the cumulative number and percentage of people who have received an updated (bivalent) COVID-19 vaccination by race/ethnicity and age group for people 5 years and over. • Data are reported weekly on Thursday and include doses administered to Saturday of the previous week. • All data in this report are preliminary. Data for previous weeks may be changed because of delays in reporting, deduplication, or correction of errors. • The table groups people based on their current age and excludes people known to be deceased. • The analyses here are based on data reported to CT WiZ which is the immunization information system for CT. Connecticut COVID-19 Vaccine Program providers are required to report to CT WiZ all COVID-19 doses administered in CT including to CT residents and to residents of other jurisdictions. CT Wiz also receives records on CT residents vaccinated in other jurisdictions and by federal entities which share data with CT WiZ electronically (currently: RI, NJ, New York City, DE, Philadelphia, NV, Indian Health Service, Department of Veterans Affairs (doses administered since 11/2022)). Electronic data exchange is being added jurisdiction-by-jurisdiction. Once a jurisdiction is added to CT WiZ, the records for residents of that jurisdiction vaccinated in CT are removed. For example, when CT residents vaccinated in NYC were added, NYC residents vaccinated in CT were removed. • Population size estimates used to calculate cumulative percentages are based on 2020 DPH provisional census estimates*. • Race and ethnicity data may be self-reported or taken from an existing electronic health care record. Reported race and ethnicity information is used to create a single race/ethnicity variable. People with Hispanic ethnicity are classified as Hispanic regardless of reported race. People with a missing ethnicity are classified as non-Hispanic. People with more than one race are classified as multiple races. A vaccine coverage percentage cannot be calculated for people classified as NH (non-Hispanic) Other race or NH Unknown race since there are no population size estimates for these groups. Data quality assurance activities suggest that in at least some cases NH Other may represent a missing value. Vaccine coverage estimates in specific race/ethnicity groups may be underestimated as result of the classification of records as NH Unknown Race or NH Other Race. • Cumulative percentage estimates have been capped at 100%. Observed percentages may be higher than 100% for multiple reasons, inaccuracies in the census denominators or reporting errors. DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020, State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

Context

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. They are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Content

As described on the NYTimes Github page.

For each date, we show the cumulative number of confirmed cases and deaths as reported that day in that county or state. All cases and deaths are counted on the date they are first announced.

In some instances, we report data from multiple counties or other non-county geographies as a single county. For instance, we report a single value for New York City, comprising the cases for New York, Kings, Queens, Bronx and Richmond Counties. In these instances the FIPS code field will be empty. (We may assign FIPS codes to these geographies in the future.) See the list of geographic exceptions.

Cities like St. Louis and Baltimore that are administered separately from an adjacent county of the same name are counted separately.

“Unknown” Counties Many state health departments choose to report cases separately when the patient’s county of residence is unknown or pending determination. In these instances, we record the county name as “Unknown.” As more information about these cases becomes available, the cumulative number of cases in “Unknown” counties may fluctuate.

Sometimes, cases are first reported in one county and then moved to another county. As a result, the cumulative number of cases may change for a given county.

Geographic Exceptions New York City All cases for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) are assigned to a single area called New York City.

Kansas City, Mo. Four counties (Cass, Clay, Jackson and Platte) overlap the municipality of Kansas City, Mo. The cases and deaths that we show for these four counties are only for the portions exclusive of Kansas City. Cases and deaths for Kansas City are reported as their own line.

Joplin, Mo. Joplin is reported separately from Jasper and Newton Counties.

Chicago All cases and deaths for Chicago are reported as part of Cook County.

Acknowledgements

Thanks to the New York Times for providing this data. The Gitbub repository can be found here: https://github.com/nytimes/covid-19-data

Data extracted from records of tickets on file with NYS DMV. The tickets were issued to motorists for violations of: NYS Vehicle & Traffic Law (VTL), Thruway Rules and Regulations, Tax Law, Transportation Law, Parks and Recreation Regulations, Local New York City Traffic Ordinances, and NYS Penal Law pertaining to the involvement of a motor vehicle in acts of assault, homicide, manslaughter and criminal negligence resulting in injury or death.

Graph and download economic data for Resident Population in Suffolk County, NY (NYSUFF0POP) from 1970 to 2024 about Suffolk County, NY; New York; NY; residents; population; and USA.

SFARI_EEG multi-paradigm dataset (Raw)

Overview

This dataset provides raw EEG from children recorded across seven complementary paradigms designed to assay neuro-oscillatory function spanning basic sensory through cognitive control and motor systems. This dataset contains data for three groups of children: 44 typically developing (TD), 66 autism spectrum disorder (ASD), and 28 sibling of individuals with ASD (SIB) participants, all between 8 and 13 years of age. Data are organized in BIDS 1.10.1 format (Dataset Type: raw).

The scientific motivation is to enable robust, large-sample mapping of oscillatory dysfunction in autism spectrum disorder (ASD) across the major frequency bands (theta, alpha, beta, gamma) using a common acquisition platform and harmonized annotations. By sampling multiple assays within the same participants, these data support both targeted hypothesis-driven analyses and data-driven discovery (e.g., network/feature selection approaches for biomarker development and predictive modeling of dimensional traits relevant to social cognition and motor function).

Acquisition

• System: BioSemi ActiveTwo, 64 channels (BioSemi64 montage)
• Sampling rate: 512 Hz
• Power line frequency: 60 Hz
• Trigger channel: Status (BioSemi)

Participants

• To be included in the ASD group, participants had to meet diagnostic criteria for ASD on the basis of the following measures: 1) autism diagnostic observation schedule 2 (ADOS-2) (Lord et al., 1994); 2) diagnostic criteria for autistic disorder from the Diagnostic and Statistical Manual of Mental Disorders (DSM-5); 3) clinical impression of a licensed clinician with extensive experience in diagnosis and evaluation of children with ASD. Due to precautions during the COVID-19 pandemic, a subset of ASD participants (n=9) was not able to complete the ADOS-2 evaluation, as masking requirements impacted administration. These participants instead underwent the Childhood Autism Rating Scale 2 (CARS-2) and Autism Diagnostic Interview-Revised (ADI-R) for diagnostic assessment. Participants in the TD group met the following inclusion criteria: no history of neurological, developmental, or psychiatric disorders, no first-degree relatives diagnosed with ASD, and enrollment in an age-appropriate grade in school. The SIB group participants met the same criteria as the TD group, except that they had a sibling diagnosed with ASD. Exclusion criteria for all groups included: (1) a known genetic syndrome associated with an IDD (including syndromic forms of ASD), (2) a history of or current use of medication for seizures in the past 2 years, (3) significant physical limitations (e.g., vision or hearing impairments, as screened over the phone and on the day of testing), (4) premature birth (<35 weeks) or having experienced significant prenatal/perinatal complications, or (5) a Full Scale IQ (FS-IQ) of less than 80.

• See participants.tsv and participants.json in each specific paradigm for more details.

Data quality & preprocessing notes

Raw EEG is provided without preprocessing.

Notes

• This work was supported by a grant from the Simons Foundation Autism Research Initiative (SFARI Award # 874845, SM). Support for recruitment and phenotyping of participants was provided by the Human Clinical Phenotyping Core of the NICHD funded Rose. F. Kennedy Intellectual and Developmental Disabilities Research Center (P50 HD105352, SM)

Description of the tasks

Auditory Steady-State Response (ASSR_run)

EEG recorded during an Auditory Steady-State Response (ASSR) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). Auditory stimuli were 500-ms binaural click trains at either 27- or 40-Hz, presented through HD 650 Sennheiser headphones at 60 dB SPL. Inter-stimulus interval was randomly jittered between 488-788 ms. On 15% of trials, an oddball stimulus presented at a different frequency (27-Hz for 40-Hz trials, 40-Hz for 27-Hz trials) was randomly intermixed among the standards. Participants were instructed to respond via button-press when they identified an oddball stimulus, to promote attention to the auditory stimuli. Stimuli were presented in four randomly presented blocks of 100 trials—blocked by stimulus type (40-Hz standard, 27-Hz standard), consisting of 170 trials per standard frequency and 30 trials per oddball stimulus.

  Events:

• Codes: '27_Hz_Standard': 21, '40_Hz_Oddball': 12, '40_Hz_Standard': 11, '27_Hz_Oddball': 22, 'Block_27_Hz_Standard': 27, 'Block_40_Hz_Standard': 40, 'Half_Block_Pause': 199, 'Response_button':1}

• Onsets are stimulus onsets derived from the Status channel.

• See each *_events.tsv for per-run details.

Notes: - Please cite:

Face Processing (FAST_run)

EEG recorded during a social attentional task (FAST) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). The stimuli, controlled by Presentation software (Neurobehavioral Systems), were faces ('Social') or objects (‘Non-Social’), each shown as upright and inverted images, along with shadow versions. Participants were instructed to press a button as quickly as possible upon detecting a shadow stimulus (presented at 20% probability). A jittered interstimulus interval (900–1100ms) reduced onset predictability. The task comprised 720 trials across 12 blocks (60 trials/block, ~3 minutes and 40 seconds each). Blocks were organized by stimulus category; each block contained only social stimuli (i.e., upright and inverted faces with their shadow versions) or non-social stimuli. There were six blocks of social and six blocks of non-social in total). Face and object images (upright/inverted) were randomly chosen from a pool of 28 stimuli. All faces depicted a positive emotion, (i.e., smiling faces; see the github folder for stimuli). Shadow faces and objects were chosen across a reduced pool of 5 stimuli. Responses were recorded using a response pad (Logitech Wingman Precision Gamepad), and stimulus and response triggers were sent from the PC acquisition computer via Presentation software.

Events: - Codes: Face_upright=21, Face_inverted=22, Face_upright_shadow=121, Face_inverted_shadow=122, Object_upright=31, Object_inverted=32, Object_upright_shadow=131, Object_inverted_shadow=132 - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Note: - Please cite: in preparation

Intersensory Attention (Beepflash_run) Cued S1→S2 design indicating whether to attend visual or auditory targets. Primary measures: posterior alpha increases indexing suppression of task-irrelevant sensory input and intersensory attentional gating.

Audiovisual simple reaction time task (AVSRT_run)

EEG recorded during an audiovisual simple reaction-time task (AVSRT) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). The stimuli, controlled by Presentation software (Neurobehavioral Systems), included three types: a red disc ('Visual'), a 1000Hz tone ('Audio'), and their simultaneous presentation ('Audiovisual'). Participants were instructed to press a button as quickly as possible upon detecting any stimulus. The auditory stimulus was 1000Hz, 60ms tone presented binaurally (75 dB SPL). The visual stimulus was a red disc subtending to 1.5 degrees, displayed above a fixation cross. The audiovisual stimulus was a simultaneous presentation of both. Each trial presented a pseudo randomly chosen stimulus (A, V, or AV; represented equiprobably), with stimuli delivered through headphones (XX) and displayed on a flat-panel LCD (60Hz). A jittered randomly sampled interstimulus interval (1000-3000ms) reduced onset predictability. The task consisted of 400 trials across 4 blocks (100 trials per block), each block lasting approximately 3 minutes and 40 seconds. Button presses were recorded using a response pad (Logitech Wingman Precision Gamepad). Triggers indicating stimulus latency were sent from the PC acquisition computer via Presentation software.

Events: - Codes: AV=3, A=4, V=5 - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Note: - Please cite: in preparation

Motor Processing (Motor_run)

EEG recorded during a mobile EEG paradigm in children. This paradigm is recorded using Lab Streaming Layer to synchronize the camera system (for gait measures) with the EEG recordings.

• Participants are either standing on a treadmill (30 sec recordings) or walking on a treadmill (5 min recordings) with either no flow (static background with white dots) or a flow (dots are moving toward the participant). The task is to ignore the white dots and to respond when the fixation cross at the center of the projected image rotate (45 degrees rotation).

Events: - Codes: - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Cross-sensory attentional switching task (Beep-Flash_run)

EEG recorded during a cross-sensory attentional task (Beep-Flash) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). A cued intersensory attention task was employed in which each trial consisted of an instructional cue (S1), an

This table will no longer be updated after 5/30/2024 given the end of the 2023-2024 viral respiratory vaccine season. This table shows the number of CT residents who received an updated 2023-2024 COVID-19 vaccination by week and age group (current age). Only the first dose is counted. CDC recommends that people get at least one dose of this vaccine to protect against serious illness, whether or not they have had a COVID-19 vaccination before. Children and people with moderate to severe immunosuppression might be recommended more than one dose. For more information on COVID-19 vaccination recommendations, click here. • Data are reported weekly on Thursday and include doses administered to Saturday of the previous week (Sunday – Saturday). All data in this report are preliminary. Data from the previous week may be changed because of delays in reporting, deduplication, or correction of errors. • These analyses are based on data reported to CT WiZ which is the immunization information system for CT. CT providers are required by law to report all doses of vaccine administered. CT WiZ also receives records on CT residents vaccinated in other jurisdictions and by federal entities which share data with CT Wiz electronically. Electronic data exchange is being added jurisdiction-by-jurisdiction. Currently, this includes Rhode Island and New York City but not Massachusetts and New York State. Therefore, doses administered to CT residents in neighboring towns in Massachusetts and New York State will not be included. A full list of the jurisdiction with which CT has established electronic data exchange can be seen at the bottom of this page (https://portal.ct.gov/immunization/Knowledge-Base/Articles/Vaccine-Providers/CT-WiZ-for-Vaccine-Providers-and-Training/Query-and-Response-functionality-in-CT-WiZ?language=en_US) • People are included if they have an active jurisdictional status in CT WiZ at the time weekly data are pulled. This excludes people who live out of state, are deceased and a small percentage who have opted out of CT WiZ.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Power your US Operations, HR Tech, and Market Intelligence engines with the most comprehensive database of the American workforce. This dataset offers a structured, historical view of 194,408,909 US professionals, capturing career trajectories, educational backgrounds, and skill sets across every state and industry.

With coverage nearing 100% of the active US white-collar workforce, our US Professional Identity Graph provides a dynamic view of talent. We map the relationships between People, Companies, Skills, and Schools, allowing you to answer complex questions about domestic talent migration, skill supply, and organizational hierarchies.

Key Use Cases 1. B2B Data Enrichment & CRM Hygiene Turn a simple email address or name into a full 360-degree US prospect profile.

Append: Add currentCompanies, jobTitle, and industry to your existing US leads.

Lead Scoring: Use connectionsCount and recommendations as proxies for influence within US markets.

Refresh: Identify when a prospect has changed jobs (lastUpdated) to trigger "New Role" outreach campaigns.

1. Talent Intelligence & Recruitment Build the next generation of US hiring tools.

Sourcing: Query by complex skill combinations (e.g., "Python" + "TensorFlow" + "5 Years Experience" in "San Francisco").

Alumni Targeting: Use educations data to find candidates from specific US Universities (Ivy League, State Colleges, etc.).

DEI Analytics: Leverage pronoun and volunteerExperiences data for diversity and inclusion benchmarking.

1. US Labor Market Analysis

Migration Trends: Track talent movement between states (e.g., "Tech talent moving from CA to TX").

Skill Trends: Analyze the rise of specific skills across US industries.

Data Dictionary & Schema Attributes Our schema is normalized for easy ingestion. We provide over 30 rich attributes per profile, grouped into five core intelligence clusters:

1. Identity & Social (The "Who")

publicId / vanity: The unique handle for the profile (e.g., /in/john-doe).

urn: The immutable, system-unique identifier.

fullName, firstName, lastName: Parsed name fields.

headline & summary: The professional's self-described bio and taglines.

pronoun: Self-identified pronouns.

logoUrl: Profile image link.

openToWork: Indicator of active job-seeking status.

1. Professional Graph (The "What")

currentCompanies: Detailed object containing Company Name, Title, Start Date.

previousCompanies: Historical array of past roles, creating a full resume view.

industry: Standardized industry classification.

1. Capability & Skills (The "How")

skills: Array of endorsed skills (e.g., "Project Management", "SQL").

languages: Spoken languages and proficiency levels.

certifications: Professional licenses and validity dates.

courses & honors: Academic and professional awards.

educations: Full academic history including Degree, School, and Dates.

1. Influence & Content (The "Reach")

connectionsCount: Total network size.

followersCount: Measure of audience reach.

recommendations: Text of received professional endorsements.

organizations: Memberships in professional bodies or non-profits.

patents, projects, publications: Intellectual property and portfolio items.

1. Location & Metadata

locationName: City/Metro area (e.g., "Greater New York City Area", "Austin, Texas").

locationCountry: Fixed to "US".

lastUpdated: Timestamp of the most recent data refresh.

id: 194408909 - Fill Rate: 100% fullName: 194392269 - Fill Rate: 99.99% firstName: 194391083 - Fill Rate: 99.99% lastName: 193031965 - Fill Rate: 99.29% publicId: 194408909 - Fill Rate: 100% urn: 194408909 - Fill Rate: 100% headline: 194260405 - Fill Rate: 99.92% summary: 41525593 - Fill Rate: 21.36% industry: 143067057 - Fill Rate: 73.59% locationName: 194408824 - Fill Rate: 100% locationCountry: 194408909 - Fill Rate: 100% logoUrl: 62644925 - Fill Rate: 32.22% connectionsCount: 139069652 - Fill Rate: 71.53% followersCount: 140881048 - Fill Rate: 72.47% currentCompanies: 133983286 - Fill Rate: 68.92% previousCompanies: 67758867 - Fill Rate: 34.85% educations: 88604497 - Fill Rate: 45.58% volunteerExperiences: 12375279 - Fill Rate: 6.37% skills: 75429843 - Fill Rate: 38.8% pronoun: 14806274 - Fill Rate: 7.62% related: 141341109 - Fill Rate: 72.7% languages: 14267971 - Fill Rate: 7.34% recommendations: 10304568 - Fill Rate: 5.3% certifications: 19279558 - Fill Rate: 9.92% courses: 5153692 - Fill Rate: 2.65% honors: 7139463 - Fill Rate: 3.67% organizations: 6840143 - Fill Rate: 3.52% patents: 411407 - Fill Rate: 0.21% projects: 4099324 - Fill Rate: 2.11% publications: 2927800 - Fill Rate: 1.51% lastUpdated: 194408909 - Fill Rate: 100% member_id: 193803832 - Fill Rate: 99.69% company_id: 85095974 - Fill Rate: 43.77% num_recommenders: 10304568 - Fill Rate: 5.3% experiences_count: 146291011 - Fill Rate: 75.25% educations_count: 88604834 - Fill Rate: 45.58% linkedin_name: 194408909 - Fill Rate: 100% endorsers: 6508123 - Fill Rate: 3.35% open_to_work: 6433122 - Fill Rate: 3.3...

Context

DA_Avocado_PJ is a personal data analysis project, was create based on the original Avocado Prices data from the Hass Avocado Board (an U.S avocado database) posted on Kaggle by Justin Kiggins (2018) and updated to 2020 by TIMOFEI KORNEV. Finally updated to 2022 by me.

In this project, I will conduct an analysis of the avocado market in the US, helping businesses understand the avocado market in the US over the years and development orientation for business in the future by analyzing Price, Volume Sold, Revenue of avocado in U.S.

In this analysis I will solve 3 main problems:

• How is the avocado market in the US from 2015 to 2022?
• What kind of avocado should be sold?
• Where we should sell that avocado?

About data

date: The date of the observation geography: The city or region of the observation total_volume: Total number of avocados sold average_price: The average price of a single avocado _4046,_4225,_4770: Total number of avocados with PLU 4046,4225,4770 sold type : Conventional or organic

Prepare data

First, I need to update this data to 2022. Because the original data is only updated from 2015 to 2020.

After that, I categorize the dataset into 2 types:

• avocado_isUS_2022: Is a dataset representing totals across the United States

• avocado_notUS_2022: Is a dataset showing only cities and regions in the United States

But

After looking through the data, I recognize that the geography column in avocado_notUs_2022 was mixed between region and city,

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midsouth : is a region include many cities but Hartford/Springfield is two big cities in Connecticut

So I decided to separate it.

Then I reviewed and removed some blank, negative values in two final dataset.

And Finally, we have

• avocado_isUS_2022: Overall data on the US, used to analyze the assessment of the avocado market in the US from 2015 - 2022

• avocado_detail: Data only includes cities from 2015 - 2022

Analysis

1. An overview analysis report on the avocado market in the US from 2015 to 2022

(Overview Eng)

(Overview Vie)

The results show that the US avocado market has just gone through a major crisis in 2020 and is showing signs of recovery. This sign of recovery is strongly expressed in Organic avocados, especially in the 4770 type. The analysis also shows that there is a trend towards organic avocado varieties after the crisis, even though they are more expensive. The analysis results show that the best time to sell avocados is from early spring to the end of summer.

1. In-depth analysis of avocado market through cities, focusing on Organic avocado varieties

(Deep Eng)

(Deep Vie)

In this analysis we will only focus on the Organic variety, because of its prominence in the previous analysis. In addition, 2020 will be the base mark for this analysis, to show how the recovery level of each city varies.

Top 5 cities with the highest revenue from Organic avocados in the last 3 years 1. New York 2. Los Angeles 3. San Francisco 4. Seattle 5. Portland

The analysis results show that Seattle is really a potential city for participating in the avocado market in the US, with the dominance in volume as well as the highest selling price in 2022.

In this project I also created a dynamic dashboard by Power BI but sadly is it's in pbix file and hard for me while Microsoft to limit the dashboard to only pbix or pdf so I can't share it 😭😭😭

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Information on the fetal death data tape file was abstracted from the Report of Fetal Death forms received in all the States and the District of Columbia, with a record on the data file for each report of a fetal death received. The data is provided to the National Center for Health Statistics (NCHS) through the Vital Statistics Cooperative Program by the registration offices of all States, the District of Columbia, and New York City. Data from New York, excluding New York City, were submitte d in machine readable form. All other 1992 data were coded and keyed by the U.S. Bureau of the Census. Fetal death data are limited to deaths occurring within the United States to U.S. residents and nonresidents. Fetal deaths occurring to U.S. citizens outside the United States are not included in this data file. In NCHS tabulations by place of residence, fetal deaths to nonresidents of the United States are excluded. The foreign resident records can be identified by code 4 in tape location 7 of the data tape. In addition, the majority of fetal death tables published by NCHS include only those fetal deaths with stated or presumed gestation of 20 weeks or more (see the Technical Appendix). Those records identified with a 2 in tape location 5 are included in these tabulations. All other records are excluded. Effective January 1, 1989, a revised U-S. Standard Report of Fetal Death replaced the 1978 revision. The 1989 revision provides a wide variety of new information on maternal and fetal health characteristics. Questions on complications of labor and delivery and congenital anomalies of fetus were changed from an open-ended question to a checkbox format to improve reporting of information. Several new items were added that improve the data files value for monitoring and research of factors affecting fetal mortality. The Office of Management and Budget revised its designation of metropolitan statistical areas based on figures from the 1990 Census. Effective with the 1990 data file, NCHS has been using these new definitions and codes as indicated in the listing of 320 Metropolitan Statistical Areas (MSAS), Primary Metropolitan Statistical Areas (PMSAS), and New England County Metropolitan Ar eas (NEaSS) included in this documentation. There are also 20 Consolidated Metropolitan Statistical Areas (mSAS), which are made up of PMSAS. Other geographic changes based on the 1990 Census will be implemented later. NCHS has adopted a new policy on release of vital statistics unit record data files. This new policy was implemented with the 1989 vital event files to prevent the inadvertent disclosure of individuals and institutions. As a result, this file does not contain the actual day of the death. The geographic detail is also restricted-only counties and cities of 100,000 or more population based on the 1980 Census as well as metropolitan areas of 100,000 or more population based on the 1990 Census, are identified. NOSB = Note to Users: This CD is part of a collection located in the Data Archive at the Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items may be checked out for a period of two weeks. Loan forms are located adjacent to the collection.